The integrity of geological formations is critically affected when external forces surpass the rock’s inherent resistance, particularly during oil and gas extraction. Insufficient formation strength to counteract declining flow pressure can result in the migration of sand particles towards the wellbore. The selection of sand control techniques is influenced by a multitude of factors, including well and reservoir conditions, operational methodologies, resource availability, and economic viability. A prevalent chemical strategy for mitigating sand migration in oil reservoirs involves the in-situ consolidation of sand using resin. This technique effectively bonds sand particles within the reservoir through an auxiliary substance, thereby minimizing sand production. In this study, we explore the efficacy of various resins—furan, epoxy, melamine formaldehyde, urea formaldehyde, and vinyl ester—on the chemical consolidation of sandstone reservoirs with high clay content for the first time. The presence of clay poses significant challenges to achieving effective polymer bonding due to its interference with both sand particle cohesion and consolidation fluid penetration. Furthermore, clay expansion can hinder enhancements in compressive strength. Our tests under static and dynamic conditions demonstrated that furan and epoxy resins yielded promising results: furan resin achieved a residual permeability of 79% with a compressive strength of 1668 psi; epoxy resin exhibited a residual permeability of 62% with a compressive strength of 1579 psi. To comprehensively evaluate resin performance, additional assessments were conducted, including wettability tests, FESEM analysis, viscosity measurements, and CT imaging. These findings provide valuable insights into optimizing chemical consolidation strategies in clay-rich sandstone reservoirs.

The precise control of gelation time in polymer gel systems is critical for successful temporary blockage operations in oil and gas reservoirs, which are essential for conformance control and enhanced oil recovery. However, predicting gelation time is challenging due to complex, non-linear interactions between multiple chemical and physical parameters. Traditional laboratory-based trial-and-error methods are time-consuming and costly, creating a need for robust predictive models to streamline experimental design and field application. This study addresses a significant gap in the literature by presenting the comprehensive comparative analysis of machine learning models for predicting polymer gelation time. Nine distinct algorithms including Linear Regression, Support Vector Regression, K-Nearest Neighbors, Bayesian Ridge, Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost, were systematically trained and their prediction capability was examined. The models utilized five key experimentally measured input features: polymer concentration (%), Hexamethylenetetramine concentration (%), Catechol concentration (%), NaCl concentration (%), and viscosity (mPa & sdot;s). Model performance was rigorously assessed using standard statistical metrics (R2, MAE, MSE, RMSE), residual analysis, and sensitivity analysis to determine the influence of each input variable. The results demonstrate that while all models exhibited strong predictive capabilities, the advanced gradient boosting ensembles, particularly CatBoost, significantly outperformed other algorithms. CatBoost showed reliable accuracy(R2=0.99), tighter clustering in predicted versus actual plots, and a more physically realistic response in sensitivity analyses, thereby capturing the complex, non-linear dependencies inherent in the gelation process with greater consistency and interpretability than the other tested models.

Abstract Accurate estimation of the target oil production rate is crucial for optimizing reservoir management and maximizing recovery in mature fields, yet traditional numerical simulation methods are often computationally expensive and time-consuming. This study evaluates the predictive performance of four distinct machine learning algorithms—Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Artificial Neural Network (ANN)—to forecast the target production rate utilizing a dataset of 60 Iranian oil wells. By integrating eight key wellhead and reservoir parameters, including pressure, temperature, fluid properties, and productivity index, the research addresses the challenges of modeling complex non-linear relationships within limited datasets. The comparative analysis reveals that AdaBoost achieved superior generalization capability, yielding the highest coefficient of determination (R2=0.8727) and the lowest Root Mean Squared Error (RMSE) on the unseen testing data. While XGBoost demonstrated high accuracy, it exhibited signs of overfitting, whereas the ANN model significantly underperformed (R2=0.4372), highlighting the limitations of deep learning architectures when applied to small sample sizes compared to the robustness of ensemble methods. Furthermore, a comprehensive One-Factor-At-A-Time (OFAT) sensitivity analysis confirmed the physical consistency of the developed AdaBoost model, identifying the current oil production rate and Productivity Index (PI) as the dominant drivers of the target potential, while effectively handling noise in secondary parameters. These findings suggest that boosting algorithms provide a reliable, interpretable, and computationally efficient alternative to complex simulations for rapid production forecasting in data-constrained brownfields.

Sand production remains a critical challenge in petroleum engineering, particularly in unconsolidated sandstone reservoirs where reservoir pressure decline disrupts the stress equilibrium around the wellbore. Progressive depletion of reservoir pressure increases effective overburden stress, frequently inducing formation failure and mobilization of sand grains. Conventional mechanical sand-control methods, such as gravel packing and screens, are widely used but are costly, operationally complex, and less adaptable under harsh downhole conditions. Consequently, chemical consolidation using polymeric resins has attracted increasing attention as a more economical and technically flexible alternative. This study evaluates the performance of a furan-based polymer system for chemical consolidation of sandstone formations. At an optimized formulation of 20% CF and 18% CLA, the polymer demonstrated strong bonding capability, providing effective grain-to-grain adhesion while maintaining compatibility with reservoir brines, preserving initial rock wettability, and resisting thermal degradation. To further enhance consolidation efficiency, SiO₂ nanoparticles were incorporated into the chemical at concentrations ranging from 0.25 to 1.0 wt%. Experimental core tests revealed that nanoparticle inclusion significantly increased unconfined compressive strength, achieving values up to 2130 psi, while retaining as much as 81% of initial permeability. In addition, thermal stability was markedly improved, with high-temperature weight loss at 500°C reduced from ~ 50% to ~ 30%. Scanning electron microscopy confirmed enhanced polymer adhesion to sand particle surfaces. These findings demonstrate that nanoparticle-modified furan polymers represent a technically robust and field-viable alternative for chemical sand consolidation, with the potential to improve wellbore stability and reduce operational costs in unconsolidated petroleum reservoirs.

Abstract Clay swelling during well completion operations poses severe risks to productivity and wellbore integrity in water-sensitive formations. To address this, this study systematically evaluates the synergistic inhibitive performance of novel salt-polyol hybrid systems for high-performance water-based completion fluids. Using comparative swelling analysis, various electrolytes were assessed in combination with organic polyols. The results reveal a "Dual-Action" stabilization mechanism, demonstrating that optimal inhibition requires balancing high ionic strength to compress the electrical double layer (EDL) with specific organic adsorption to encapsulate the clay surface. A critical mechanistic discovery is the "Sorbitol Paradox": while Sorbitol performs poorly as a standalone additive due to steric propping of clay layers, it achieves optimal stabilization when combined with potassium salts. Ultimately, this mechanistic framework provides practical insights for designing optimized, non-damaging completion brines tailored to complex mineralogies.

Well completion operations involve critical post-drilling processes to enable hydrocarbon extraction, where the selection of completion fluids-such as packer, workover, or fracturing fluids-plays a pivotal role in operational success. In Iran's oil industry, high-temperature, high-pressure reservoirs necessitate high-density brines like calcium bromide. Reservoirs exceeding 300 °F and 10,000 psi (HPHT conditions) typically require completion fluids with densities above 100 lb/ft3. However, economic constraints and limited domestic bromine resources render such fluids prohibitively expensive, while locally available brines often lack essential completion fluid properties. This study addresses these challenges by synthesizing cost-effective, potassium-based brines using domestically sourced salts and alcohols to enhance density and performance. By incorporating alcohols (40% vol.), the crystallization temperature of medium-density brines was significantly reduced, enabling higher salt dissolution and achieving densities of 93.4, 97.8, 99.2, and 99.4 lb/ft3. Alcohol-free variants (93.4 and 97.8 lb/ft3) and alcohol-enhanced formulations (99.2 and 99.4 lb/ft3) demonstrated alkaline pH stability, low viscosity (Viscosity below 70 cP for pumping downhole using available pumps in Iran), minimal clay swelling (< 5 mL/2 g bentonite), and near-zero corrosion rates, even at 300 °F. Notably, exposure to reservoir rock altered wettability from oil-wet to water-wet, enhancing hydrocarbon recovery. Economically, these fluids leverage Iran's accessible raw materials, offering a 40% cost reduction compared to calcium bromide. Designed primarily as packer fluids, they ensure well integrity under high reservoir pressures while mitigating formation damage. This research presents a scalable, sustainable solution for Iran's oil sector, balancing technical efficacy with economic viability in challenging downhole environments.

Permeability reduction is one of the significant problems in the water injection process. Several mechanisms may be involved in permeability reduction including scale formation, suspended particle invasion and fines migration. On the other hand, rock dissolution may also lead to permeability improvement of carbonate rocks. In this research, effect of rock lithology along with incompatibility of injection and formation brines, scale formation and plugging of pores by particles in the injection water for a layered reservoir were examined. Diluted formation brine (produced from desalination unit) was considered as injection water. Simulation of scale formation due to thermodynamic conditions and mixing of formation and injection brines were performed to investigate amount and types of scales. Simulation and jar tests showed that Scale precipitation decreased with increasing the mixing ratio of injection water to 75%. For core flooding tests, both carbonate and sandstone core samples from a layered candidate reservoir were selected for monitoring of formation damage and permeability variation due to incompatibility of fluids. SEM and EDX analysis were used for static and dynamic tests. Carbonate core sample showed sever damage and competition between scale precipitation and rock dissolution. Higher flow rates contribute to permeability enhancement of carbonate rocks by prevention of scale sedimentation (less scale formation) and acceleration of carbonate rock dissolution. As a pressure difference increasing of 60% was shown in the first stage of water injection process into the carbonate rock. Eventually, after increasing and decreasing the injection rate, the pressure difference decreased to 30 presences. Scale formation mechanism is dominant in sandstone rock samples and can be removed by backflow. As pressure difference decreased 27% in sandstone rock sample, which resulted in permeability decrease from 365 to 265 mD.

The oil and gas industry is continuously seeking advanced methods to optimize reservoir productivity. Among the techniques employed, matrix acidizing, particularly using Viscoelastic Diverting Acid (VDA), has shown significant promise in enhancing permeability and improving well performance. However, the absence of standardized formulations for VDA requires a careful evaluation of commercial additives to ensure their reliability and effectiveness in diverse reservoir conditions. This article presents a comprehensive case study that examining the application of hydrochloric acid (HCl) and VDA systems in a low-permeability carbonate reservoir situated in southern Iran. The study integrates design of experiments (DOE) and multi-objective optimization techniques to assess the impact of various operational parameters on acidizing performance. The research focused on high-temperature formations, employing multi-stage acid injections to evaluate the effectiveness of the acid treatments. The use of DoE and response surface methodology (RSM) provided a structured approach for optimizing key parameters to enhance treatment outcomes. The results of this study demonstrated a substantial increase in both oil production and well pressure following the acidizing process. Specifically, oil production improved by 80%, and well pressure increased by 73%, underscoring the effectiveness of the acid treatment in stimulating the reservoir. The VDA system, combined with the bullheading injection technique, facilitated superior acid placement and distribution within the reservoir, enhancing the overall acidizing efficiency. Furthermore, the optimization of operational parameters such as skin factor and maximum invasion depth through the use of RSM allowed for a significant refinement of the acidizing process. The developed models, with correlation coefficients exceeding 0.96, further validated the accuracy of the optimization process and provided valuable insights into the critical factors influencing acidizing performance. Additionally, the refined acidizing program minimized operational uncertainties, leading to improved treatment efficiency and more consistent results in challenging high-temperature formations.

Over the past decade, smart water injection has emerged as a promising enhanced oil recovery (EOR) technology for sandstone and carbonate reservoirs, offering an environmentally friendly alternative that avoids the use of costly and toxic chemicals. Numerous studies have identified wettability alteration-from oil-wet to water-wet states-as the primary mechanism underlying increased oil production in sandstone formations. However, incompatibility between smart water compositions and both reservoir fluids and minerals can lead to undesired precipitate formation, adversely affecting reservoir performance. In weakly consolidated sandstone reservoirs, the interaction between injected smart water and reservoir rock disrupts ion equilibrium, promoting the dissolution of essential minerals such as quartz and intergranular cement. This process commonly results in rock matrix weakening and mobilization of sand particles, which not only reduce permeability but also cause significant formation damage by blocking flow pathways. Therefore, optimizing the ionic composition of smart water is crucial to achieve maximum oil recovery while mitigating mineral dissolution and sand production. This study introduces an integrated methodology for the assessment and optimization of smart water injection in unconsolidated sandstones, highlighting the importance of balancing wettability alteration with geochemical stability. Our findings demonstrate that simultaneous modeling of fluid flow and mineral reactions accurately predicts sand production trends and their implications for reservoir performance. The proposed workflow effectively identifies brine compositions that enhance oil recovery and minimize formation damage, offering valuable guidelines for the field-scale application of smart water EOR in unconsolidated sandstone reservoirs.

Matrix acidizing is a well stimulation technique used in oil and gas reservoirs to enhance the flow of hydrocarbons. In acidizing operations, the acid reacts with the materials in the reservoir rock, causing an increase in existing pores or creating new ones, which increases the permeability of the rock and reduces formation damage. In this research conducted on the Sarajeh gas reservoir, eight different types of acid, four different injection rates, and four different acid volumes were used, resulting in a total of 128 different acidizing programs simulated by commercial software. Subsequently, modeling and optimization of the skin factor and cost parameters were performed. The model results show high accuracy for the skin factor (R2 = 0.9899) and cost (R2 = 0.9999). Analyses also indicate that in the combination of 7.5% hydrochloric acid and 9% formic acid, increasing the acid volume does not necessarily lead to better results. Adding acetic acid to 15% hydrochloric acid significantly changes the overall process compared to 7.5% hydrochloric acid and its combination with acetic acid. Ultimately, the most optimal design is the use of 15% hydrochloric acid at an injection rate of 11 barrels per minute and a volume of 477 barrels, which leads to a reduction of the shell coefficient to -1.51 based on the model's prediction; the simulation performed with software for this acidizing program shows that the skin factor decreases to -1.62, which is close to the predicted value. This approach provides a comprehensive framework for optimizing acidizing, establishing a balance between cost savings and improved operational performance.

Well productivity plays a vital role in determining the economic viability of hydrocarbon field development. Among stimulation techniques, matrix acidizing is highly effective for improving well performance by removing near-wellbore formation damage. The success of an acidizing treatment depends on the precise selection of injection volume and rate to maximize damage removal while minimizing operational costs. This study introduces an integrated computational framework for optimizing matrix acidizing processes. The framework first predicts post-treatment outcomes and then determines optimal injection parameters. Its main objective is to identify the acid volume and injection rate that minimize the post-treatment skin factor, thereby enhancing well productivity while reducing acid consumption and cost. The methodology consists of two sequential phases: prediction and optimization. In the prediction phase, four machine learning algorithms—Extra-Trees (ExTree), Random Forest, Gradient Boosting, and AdaBoost—were trained using a comprehensive dataset derived from extensive reservoir simulations to estimate post-acidizing skin factor and injection pressure. Among these models, the ExTree algorithm achieved the highest predictive accuracy, with an R2 value of 0.9390 for the skin factor. In the optimization phase, the validated ExTree model was coupled with two metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), to determine the optimal injection parameters. Both optimization algorithms consistently converged to stable solutions within 500 iterations. The framework’s computational cost is practical for engineering design and offline analysis, confirming its feasibility for field applications. The novelty of this research lies in its unified, data-driven approach that combines high-accuracy prediction with advanced optimization techniques. This integration provides a robust and efficient tool for the rational design of well stimulation treatments.

Well completion fluids are vital for hydrocarbon extraction and well integrity, particularly in high-pressure, high-temperature (HPHT) reservoirs. Conventional calcium bromide brines, though dense, face economic and logistical challenges in Iran due to import dependency. This study introduces a cost-effective, high-density alternative using locally sourced lead acetate and monoethylene glycol (MEG), achieving a density of 107 lb/ ft(3)-a 37% increase over baseline lead acetate brine (78 lb/ft(3)). The optimized formulation combines pump-ability (64.5 cP viscosity) with exceptional thermal stability: negligible corrosion (0.0004 mils/year at 300 degrees F) and controlled clay swelling (7 cc/3 g bentonite). Alcohol integration further enhanced freeze resistance (crystallization temperature <-4(degrees)F), surpassing calcium bromide brines. Despite transient turbidity at 25(degrees)C, the fluid stabilized at reservoir temperatures (300 degrees F), confirming HPHT suitability. A critical wettability shift from oil-wet to water-wet in sandstone formations was observed, suggesting potential oil recovery benefits. Economically, the fluid reduces costs by 40-50% by eliminating bromide imports. This lead acetate-MEG system offers a sustainable, high-performance solution for Iran's HPHT reservoirs, with global applicability in similar environments.

In loose sandstone reservoirs, sand enters the wellbore along with the production fluid. Sand production causes numerous problems, such as the erosion of downhole, wellhead, and surface equipment, ultimately leading to a decline in production. In this paper, the authors present a new epoxy-based nanofluid for controlling sand production. To demonstrate its effectiveness, a near-wellbore laboratory simulator system (NeWSS) was developed, which takes into account all downhole conditions, such as the radial distribution of flow, temperature, and reservoir pressure. The epoxy/g-C3N4-NS nanofluid has two special properties. First, carbon nitride nanosheets were used as an active strengthening agent to increase the compressive strength of the epoxy resin after curing. Second, a bubbling agent was used to create micro- and macro-pores, facilitating the movement of the production fluid and ultimately increasing permeability. Laboratory results showed that the optimum concentrations of the bubbling agent and g-C3N4-NS are 3 wt

This study introduces a novel phosphate-based packer fluid, designed for use in high-temperature and high-pressure oil and gas wells. The research aims to evaluate the performance of this innovative fluid in comparison with traditional acetate and formate-based fluids. The study highlights the enhanced performance metrics of the phosphate-based fluid, which include a higher density of 114 pcf, moderated pH levels from 13.5 to 10, and a significantly reduced corrosion rate to below 4 mpy, achieved through the addition of diammonium phosphate and potassium vanadate. Moreover, the research presents two machine learning models (an artificial neural network (ANN) and genetic programming (GP)) developed to predict the penetration depth of the phosphate-based fluid. Both models demonstrate high accuracy, with R-square values of 0.9468 and 0.9140, respectively, with the ANN model exhibiting slightly superior performance. The findings of the study indicate that the phosphate-based fluid, free of solubilizers and enhanced with innovative corrosion inhibitors, provides optimal thermal stability, minimal formation damage, and shallow penetration depth, thus representing a significant advancement in well completion technologies. The fluid’s distinctive properties and the predictive models’ high accuracy highlight its suitability for challenging environments, marking a notable progression in well completion technologies.

This chapter introduces a technique for predicting formation fracture pressure through geomechanical modeling. It outlines fundamental elasticity concepts and parameters, including stress, strain, and the stress–strain relationship, and their correlation with the uniaxial compressive strength of rocks. The discussion extends to pore pressure and its impact on formation fracture pressure. Additionally, it covers methods for estimating the magnitude and orientation of principal stresses in situ, utilizing tools like image logs (FMI) and elastic moduli. The chapter also details the construction of a geomechanical model, involving the determination of elastic constants, static elastic moduli, rock resistance parameters, overburden stress, horizontal in-situ stresses, Biot coefficient, in-situ stress regime, and temperature conditions. Ultimately, the chapter aims to offer a theoretical and practical framework for predicting formation fracture pressure and enhancing well design and stimulation processes.

Condensate blockage significantly impairs gas production in low-permeability reservoirs by reducing gas relative permeability and increasing condensate saturation near the wellbore. Particularly acute in reservoirs with low pressure and permeability, effective solutions are required to mitigate this formation damage. This study introduces a novel acid treatment strategy aimed at enhancing the injectivity index in gas reservoirs afflicted by condensate blockage. Leveraging mineralogical analysis, Hydrochloric Acid (HCl) was identified as the optimal acidizing agent. The most effective concentrations of HCl for rock dissolution—15% and 7.5%—were determined through dissolution tests. The research further advances by adding methanol to the acid mix, resulting in three distinct formulations: HCl 15 wt%, HCl 15 wt% + methanol, and HCl 7.5 wt% + methanol. Comprehensive wettability alteration tests and coreflood experiments were conducted to evaluate the efficacy of these systems in permeability enhancement. The HCl 7.5 wt% + methanol formulation demonstrated superior performance in permeability improvement and condensate blockage reduction, outshining the other systems. Notably, this new acid system effectively altered wettability from hydrophobic to hydrophilic, facilitating the passage of condensate through the pore throats and thus aiding in the removal of blockages. The integration of methanol with HCl, particularly at a 7.5 wt% concentration, represents a significant advancement in the treatment of condensate blockage in gas reservoirs, promising to improve gas recovery rates by addressing the challenges posed by low-permeability formations.

This chapter introduces a methodology for analyzing and comparing laboratory findings with commercial software for matrix acidizing design and enhancement. It outlines the essential information and data required for input, such as reservoir characteristics, acid composition and concentration, injection rates and volumes, and coreflood test outcomes. The software's test design technique is explained, utilizing a factorial design method to assess the impact of various variables on acidizing effectiveness. Additionally, it elaborates on determining the best injection rate and volume of acid based on the software's results, which include visual aids, data tables, and analytical reports. The chapter further scrutinizes the optimal acid selection recommended by the software in comparison to the laboratory findings, emphasizing both similarities and disparities. Ultimately, the chapter aims to furnish a practical and dependable tool for matrix acidizing design and assessment.

As the usage of geothermal energy as a zero- emission power resource continues to grow in significance, comprehending the interplay between physical and chemical processes within geothermal reservoirs becomes crucial. In this study, a computationally efficient fluid flow and heat transfer model, combined with a fluid chemistry model, is used to simulate fluid circulation and mineral precipitation in reservoir rock, resulting in changes in rock porosity and permeability. A 2D hybrid approach is employed to solve transient mass and momentum conservation equations, coupled with an analytical solution of the energy equation proposed in the literature for geological formations. A marching algorithm is utilized to calculate velocity and temperature fields in the axial direction within the production zone. Mineral scaling is addressed using the outputs of the hybrid model to perform saturation index (SI) and solution/dissolution computations for qualitative and quantitative mineral precipitation modeling. Multiple criteria are considered to assess the likelihood and intensity of fouling issues. The analysis results are used in an empirical model to estimate rock secondary porosity and permeability changes over a 5- year period of heat extraction. The developed simulator is applied to model a site in the Sabalan geothermal field in Iran, and its initial verification is conducted using data from the same site in the literature. The findings in the study for a sensitivity on fluid circulation rate reveal that increasing water circulation flow rate increases precipitation rate and pumping power required. Furthermore, even minor instances of pore blockage can result in notable reductions in permeability. Consequently, ensuring precise control over pressure and temperature during the production phase becomes progressively crucial for both reservoir integrity and production assurance. The proposed framework provides a promising approach for accurate and efficient simulation of geothermal reservoirs to optimize power generation and minimize environmental impact.

This chapter outlines a method for cost-effective evaluation and estimation of matrix acidizing projects. It details the essential expenses involved, such as acid, additives, equipment, and labor, and elucidates on how to compute them based on factors like acid type, concentration, injection rate, and volume. Moreover, it delves into estimating flow rate changes post-acidizing, considering reservoir characteristics, acid-rock interactions, and stimulation efficiency. The discussion extends to estimating costs and profits resulting from acidizing, employing net present value (NPV) as a key metric. Additionally, the chapter introduces the concept of the fold of increase (FOI), a measure of the incremental profit-to-acidizing cost ratio, and elaborates on maximizing it through well-informed decisions. Ultimately, the chapter endeavors to offer a practical and valuable tool for the economic analysis and optimization of matrix acidizing projects.